Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

Kumar, Pradeep; Grewal, Monika; Srivastava, Muktabh Mayank

doi:10.48550/arxiv.1711.08760

Cited by 3 publications

(11 citation statements)

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“…clinical practitioners. The noticeable progress in deep learning has benefited many trials in medical image analysis, such as lesion segmentation or detection [1], [2], [3], [4], [5], diseases classification [6], [7], [8], [9], noise induction [10], image annotation [11], [12], registration [13], regression [14] and so on. In this paper, we investigate the CXR classification task using deep learning.…”

mentioning

confidence: 99%

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification

Guan¹,

Huang²,

Zhong³

et al. 2018

Preprint

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This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy is limited in two aspects. 1) A thorax disease usually happens in (small) localized areas which are disease specific. Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas. 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN). AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image. The local region is used for training a local CNN branch. Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch. The comprehensive experiment is conducted on the ChestX-ray14 dataset. We first report a strong global baseline producing an average AUC of 0.841 with ResNet-50 as backbone. After combining the local cues with the global information, AG-CNN improves the average AUC to 0.868. While DenseNet-121 is used, the average AUC achieves 0.871, which is a new state of the art in the community.

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confidence: 99%

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification

Guan¹,

Huang²,

Zhong³

et al. 2018

Preprint

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“…In addition, 206,574 radiology reports that correspond to the CXR images are also available. Consistent with recent previous works on automated chest x-ray analysis (Yao et al, 2017;Rajpurkar et al, 2017;Guan et al, 2018;Kumar et al, 2017;Baltruschat et al, 2018), we focus on recognizing 14 thoracic disease categories, including atelectasis, cardiomegaly, consolidation, edema, effusion, emphysema, fibrosis, hernia, infiltration, mass, nodule, pleural thickening, pneumonia and pneumothorax. Unique from previous works, we train separate models based on the view position of the radiograph.…”

Section: Introductionmentioning

confidence: 86%

“…While computer-aided diagnosis in chest radiography has been studied for many years (Van Ginneken, 2001;, the public release of the ChestX-ray14 dataset has resulted in many recent works that attempt to classify thorax diseases from frontal chest x-rays (Yao et al, 2017;Rajpurkar et al, 2017;Guan et al, 2018;Kumar et al, 2017;Baltruschat et al, 2018). Perhaps the most well-known of these works is that of Rajpurkar et al (2017), in which the authors present the ChexNet system.…”

Section: Related Workmentioning

confidence: 99%

“…Several of the works severely downsample input images to match dimensions required for pre-trained classifiers (Rajpurkar et al, 2017;Guan et al, 2018). Moreover, the mentioned recent works (Yao et al, 2017;Rajpurkar et al, 2017;Guan et al, 2018;Kumar et al, 2017;Baltruschat et al, 2018) make no distinction between PA and AP chest x-rays view positions. This can be a problem for some findings, such as cardiomegaly, which can only be accurately assessed in PA images, as the AP view will exaggerate the heart silhouette due to magnification.…”

Section: Related Workmentioning

confidence: 99%

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Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks

Rubin,

Sanghavi,

Zhao

et al. 2018

Preprint

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The MIMIC-CXR dataset is (to date) the largest released † chest x-ray dataset consisting of 473,064 chest x-rays and 206,574 radiology reports collected from 63,478 patients. We present the results of training and evaluating a collection of deep convolutional neural networks on this dataset to recognize multiple common thorax diseases. To the best of our knowledge, this is the first work that trains CNNs for this task on such a large collection of chest x-ray images, which is over four times the size of the largest previously released chest x-ray corpus (ChestX-Ray14). We describe and evaluate individual CNN models trained on frontal and lateral CXR view types. In addition, we present a novel DualNet architecture that emulates routine clinical practice by simultaneously processing both frontal and lateral CXR images obtained from a radiological exam. Our DualNet architecture shows improved performance in recognizing findings in CXR images when compared to applying separate baseline frontal and lateral classifiers. †. At the time of writing, MIMIC-CXR has been made available as a limited release and is intended for dissemination in the near future.

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“…Shen and Gao (2018) combines the routing-by agreement mechanism and the deep convolutional neural network. To model the relationships among diseases, Kumar, Grewal, and Srivastava (2017) designs a boosted cascaded convolutional network framework which is similar to the classifier chains. Yao et al (2018) uses Densenet (Huang et al 2017) as an encoder and a Long-short Term Memory Network (LSTM) as a decoder to capture label correlations.…”

Section: A Chest X-ray Benchmarkmentioning

confidence: 99%

Label-Assemble: Leveraging Multiple Datasets with Partial Labels

Mintong¹,

Lu²,

Yuille³

et al. 2021

Preprint

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The success of deep learning relies heavily on large datasets with extensive labels, but we often only have access to several small, heterogeneous datasets associated with partial labels, particularly in the field of medical imaging. When learning from multiple datasets, existing challenges include incomparable, heterogeneous, or even conflicting labeling protocols across datasets. In this paper, we propose a new initiative-"data, assemble"-which aims to unleash the full potential of partially labeled data and enormous unlabeled data from an assembly of datasets. To accommodate the supervised learning paradigm to partial labels, we introduce a dynamic adapter that encodes multiple visual tasks and aggregates image features in a question-and-answer manner. Furthermore, we employ pseudo-labeling and consistency constraints to harness images with missing labels and to mitigate the domain gap across datasets. From proof-of-concept studies on three natural imaging datasets and rigorous evaluations on two large-scale thorax X-ray benchmarks, we discover that learning from "negative examples" facilitates both classification and segmentation of classes of interest. This sheds new light on the computer-aided diagnosis of rare diseases and emerging pandemics, wherein "positive examples" are hard to collect, yet "negative examples" are relatively easier to assemble. As a result, besides exceeding the prior art in the NIH ChestXray benchmark, our model is particularly strong in identifying diseases of minority classes, yielding over 3-point improvement on average. Remarkably, when using existing partial labels, our model performance is on-par (p > 0.05) with that using a fully curated dataset with exhaustive labels, eliminating the need for additional 40% annotation costs.

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Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

Cited by 3 publications

References 0 publications

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification

Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks

Label-Assemble: Leveraging Multiple Datasets with Partial Labels

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